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# Inequality presentation

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### Inequality presentation

1. 1. March 4th 2013 Economics Research Lounge Justus Timmers
2. 2. Glossary ①Overview i. What is the situation ii. What do we measure? ②Causation i. Differences in characteristics of people ii. Skill Biased Technological Change (sbtc) ③Other Theories ④Discussion
3. 3. Overview 0 .05 .1 .15 Density 0 10 20 30 40 50 Real Hourly Earnings (in 2002 £) 2010 CPI Adjusted Hourly Earnings 0 .05 .1 .15 Density 0 10 20 30 40 50 Hourly Earnings 2002 UK Hourly Earnings 2002 £8.00 per hour£4.40 per hour £17.50 per hour minimum wage: £4.20 minimum wage: £4.80 £8.85 per hour£4.90 per hour £18.95 per hour, all CPI adjusted 2002 £ £6.25 2010 £13.50 £5.70 £12.60 3.993 2.186 0.548 2.130 All obs p90/p10 p90/p50 p10/p50 p75/p25 Percentile ratios 3.874 2.141 0.553 2.166 All obs p90/p10 p90/p50 p10/p50 p75/p25 Percentile ratios • 9 out of 10 people we have become relatively more equal
4. 4. Overview • Income inequality has increased by just about every metric 0.09219 0.16440 0.28180 All obs A(0.5) A(1) A(2) Atkinson indices, A(e), where e > 0 is the inequality aversion parameter 0.19619 0.17961 0.22027 0.67604 0.32914 All obs GE(-1) GE(0) GE(1) GE(2) Gini sensitivity parameter, and Gini coefficient Generalized Entropy indices GE(a), where a = income difference 0.09219 0.16440 0.28180 All obs A(0.5) A(1) A(2) Atkinson indices, A(e), where e > 0 is the inequality aversion para 0.19619 0.17961 0.22027 0.67604 0.32 All obs GE(-1) GE(0) GE(1) GE(2) G sensitivity parameter, and Gini coefficient Generalized Entropy indices GE(a), where a = income difference 0.08406 0.15548 0.27456 All obs A(0.5) A(1) A(2) Atkinson indices, A(e), where e > 0 is the inequality aversion parameter 0.18924 0.16898 0.18430 0.26506 0.32144 All obs GE(-1) GE(0) GE(1) GE(2) Gini sensitivity parameter, and Gini coefficient Generalized Entropy indices GE(a), where a = income difference 0.08406 0.15548 0.27456 All obs A(0.5) A(1) A(2) Atkinson indices, A(e), where e > 0 is the inequality aversion para 0.18924 0.16898 0.18430 0.26506 0.32 All obs GE(-1) GE(0) GE(1) GE(2) G sensitivity parameter, and Gini coefficient Generalized Entropy indices GE(a), where a = income difference 2002 2010 0 .2.4.6.8 1 Cumulativeproportionofhourlypaypercapita 0 .2 .4 .6 .8 1 Cumulative proportion of population Lorenze curve 2010 Line of Perfect Equality UK Lorenz Curve 2010 0 .2.4.6.8 1 0 .2 .4 .6 .8 1 Cumulative proportion of population Lorenze curve 2002 Line of Perfect Equality UK Lorenz Curve 2002 Gini = A / A + B if pick two people at random, their income will on average differ twice the Gini coefficient of the mean. i.e. 64.2% and 65.8%, or £6.39 and £7.15 in 2002 and 2010 respectively. General Entropy (α = 1, Teil T is equal weight to all, higher(/lower) α is more attention to the top(/bottom) of the distribution) Atkinson (ε = inquality aversion, welfare loss) A B A B
5. 5. Overview • Commonly (slightly misleading) displayed as the above
6. 6. Overview • Not really the top 1%, or the top 0.1%: the very, very highest earners 100 111.9 100100 108.5 100100100 110.8 113.1 119.6 95 100 105 110 115 120 125 10th percentile 50th percentile 90th percentile 99th percentile 99.9th percentile highest 10th percentile 100 111.9 50th percentile 100 110.8 90th percentile 100 108.5 99th percentile 100 113.1 99.9th percentile 100 119.6 highest 100 527.1 2002 2010
7. 7. Approach to Equality Conservative ‘life is unfair and that is how it is’ equalopportunities equal outcomes Libertarian e.g. Milton Friedman Socialism e.g. Karl Marx • Equal outcomes (economic left) • Strife for equal opportunities (economic right) Liberalism
8. 8. Approach to Equality • Equal outcomes (economic left) • Strife for equal opportunities (economic right)
9. 9. Usual Suspects Ascribed Social Categories 0 .05 .1 .15 0 .05 .1 .15 0 10 20 30 40 50 0 10 20 30 40 50 2010 CPI Adjusted Hourly Earnings for Men 2010 CPI Adjusted Hourly Earnings for Women Density Real Hourly Earnings (in 2002 £) Graphs by gender 0 .05 .1 .15 0 .05 .1 .15 0 10 20 30 40 50 0 10 20 30 40 50 2002 UK Hourly Earnings for Men 2002 UK Hourly Earnings for Women Density Hourly Earnings Graphs by sex Gender • Women on average earned 26.6% less than men in 2002 and 22.5% than men in 2010 • The largest discrepancy is in the top earners • Closing the gap across the board • Fastest catching up at the bottom (75th percentile) • ‘Within group’ inequality is far greater than ‘between-group inequality’ • i.e. <7% (in 2002) and <4% (in 2010) of the inequality would be removed if the group means were equalised and distribution scaled proportionally
10. 10. Usual Suspects Ascribed Social Categories _cons 2.143396 .040543 52.87 0.000 2.063923 2.22287 ni .0597708 .0539356 1.11 0.268 -.0459551 .1654967 ros .1244974 .0464454 2.68 0.007 .033454 .2155409 s .0915385 .0492179 1.86 0.063 -.0049397 .1880166 w .0016495 .0479755 0.03 0.973 -.0923933 .0956924 sw .0828506 .0441603 1.88 0.061 -.0037136 .1694148 se .2178462 .0426048 5.11 0.000 .1343313 .3013611 ol .3468207 .0462804 7.49 0.000 .2561007 .4375407 il .4461553 .0518309 8.61 0.000 .344555 .5477556 eoe .1948355 .0438399 4.44 0.000 .1088996 .2807715 rowm .1464171 .0467938 3.13 0.002 .0546908 .2381435 wmmc .1492206 .0486802 3.07 0.002 .0537965 .2446446 em .0637065 .0447424 1.42 0.155 -.0239987 .1514117 royah .013041 .052325 0.25 0.803 -.0895277 .1156097 wy -.0070748 .049681 -0.14 0.887 -.1044608 .0903111 sy (dropped) ronw .0971534 .0478947 2.03 0.043 .0032691 .1910377 m -.040631 .0626806 -0.65 0.517 -.1634991 .082237 gm .0906087 .0505545 1.79 0.073 -.0084895 .1897069 rone -.063895 .0531041 -1.20 0.229 -.167991 .040201 taw -.0039876 .0563258 -0.07 0.944 -.1143987 .1064236 fb .0993994 .0255687 3.89 0.000 .0492791 .1495197 other -.0771674 .0546348 -1.41 0.158 -.1842639 .0299291 Chinese .2022185 .1609669 1.26 0.209 -.1133128 .5177498 Bdeshi -.4497993 .1621786 -2.77 0.006 -.7677059 -.1318928 Pstani -.2244202 .0891448 -2.52 0.012 -.399164 -.0496764 indian -.1241947 .0557507 -2.23 0.026 -.2334787 -.0149107 blackoth -.2383267 .1538694 -1.55 0.121 -.5399452 .0632918 African -.4293042 .0862351 -4.98 0.000 -.5983445 -.260264 caribbean -.1540092 .0735647 -2.09 0.036 -.2982125 -.0098059 female -.2834079 .0111393 -25.44 0.000 -.3052435 -.2615724 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2873.27978 9125 .314879976 Root MSE = .5312 Adj R-squared = 0.1039 Residual 2566.64371 9096 .282172792 R-squared = 0.1067 Model 306.636072 29 10.5736577 Prob > F = 0.0000 F( 29, 9096) = 37.47 Source SS df MS Number of obs = 9126 2002 _cons 2.279213 .0317056 71.89 0.000 2.217067 2.341358 ni (dropped) ros .0517595 .0353718 1.46 0.143 -.0175722 .1210913 s .0342436 .0372987 0.92 0.359 -.0388649 .1073521 w -.0339234 .0365517 -0.93 0.353 -.1055678 .037721 sw .0343687 .0339125 1.01 0.311 -.0321026 .10084 se .1720574 .0330258 5.21 0.000 .1073241 .2367906 ol .2713739 .0352879 7.69 0.000 .2022067 .3405411 il .4053498 .0388162 10.44 0.000 .3292668 .4814327 eoe .1201216 .0336419 3.57 0.000 .0541806 .1860626 rowm .0033402 .0356753 0.09 0.925 -.0665864 .0732668 wmmc -.0026973 .0377421 -0.07 0.943 -.0766749 .0712803 em -.0060544 .0341957 -0.18 0.859 -.0730809 .0609721 royah -.0017247 .038367 -0.04 0.964 -.0769271 .0734778 wy .0180399 .0368997 0.49 0.625 -.0542866 .0903665 sy -.0182286 .0405242 -0.45 0.653 -.0976594 .0612021 ronw .0301396 .0355451 0.85 0.396 -.0395318 .0998109 m -.0068597 .0414699 -0.17 0.869 -.0881441 .0744247 gm .0365281 .0366583 1.00 0.319 -.0353252 .1083814 rone -.0081565 .0399454 -0.20 0.838 -.0864529 .0701399 taw -.0789492 .0418327 -1.89 0.059 -.1609447 .0030464 fb -.0757003 .0146869 -5.15 0.000 -.1044878 -.0469128 other -.0779976 .0302154 -2.58 0.010 -.1372223 -.0187729 Chinese .0423675 .0646826 0.66 0.512 -.0844159 .1691509 Bdeshi -.4089386 .0837097 -4.89 0.000 -.5730166 -.2448605 Pstani -.1883778 .0496057 -3.80 0.000 -.285609 -.0911465 indian .0246024 .0301945 0.81 0.415 -.0345814 .0837861 blackoth -.0101515 .0653209 -0.16 0.877 -.138186 .117883 African -.1711463 .0453957 -3.77 0.000 -.2601256 -.0821669 caribbean -.1462988 .0459935 -3.18 0.001 -.2364498 -.0561478 female -.2081133 .0077271 -26.93 0.000 -.2232591 -.1929675 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 6308.19149 19890 .317153921 Root MSE = .54393 Adj R-squared = 0.0671 Residual 5876.0898 19861 .295860722 R-squared = 0.0685 Model 432.101695 29 14.9000584 Prob > F = 0.0000 F( 29, 19861) = 50.36 Source SS df MS Number of obs = 19891 2010 • Large significant effects, e.g. • Bangladeshi’s earn >40% less • Women earn over >20% less • Between-group differences in 2010 are roughly 4% for gender, 0.3% for ethnicity, 4% for region and 0% for non-UK born • Overall accounts for 10.7% of the variance in 2002 and 6.9% of the variance in 2010
11. 11. Usual Suspects Skill-Biased Technological Change University education 0 .05 .1 .15 0 .05 .1 .15 0 10 20 30 40 50 0 10 20 30 40 50 2002 UK Hourly Earnings for without University Degree 2002 UK Hourly Earnings for with University Degree Density Hourly Earnings Graphs by university degree 0 .05 .1 .15 0 .05 .1 .15 0 10 20 30 40 50 0 10 20 30 40 50 2010 CPI Adjusted Hourly Earnings for without University Degree 2010 CPI Adjusted Hourly Earnings for with University Degree Density Real Hourly Earnings (in 2002 £) Graphs by degree • Between-group differences account for about 17% • university degree holders, they have barely improved between 2002 and 2010 • large increase in degree holders, from 19.2% in 2002 to 28.3% in 2010
12. 12. Usual Suspects Skill-Biased Technological Change Age (correlated with education) 0 .1.2 0 .1.2 0 .1.2 0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50 2002 UK Hourly Earnings for ages 16 - 24 2002 UK Hourly Earnings for ages 25 - 49 2002 UK Hourly Earnings for ages 50 - 65 Density Hourly Earnings Graphs by age cohorts 0 .1.2 0 .1.2 0 .1.2 0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50 2010 CPI Adj. Hourly Earnings for ages 16 - 24 2010 CPI Adj.Hourly Earnings for ages 25 - 49 2010 CPI Adj. Hourly Earnings for ages 50 - 65 Density Real Hourly Earnings (in 2002 £) Graphs by age cohorts • The average salary of a young person has barely improved • Removing ‘between group’ could remove inequality by 8.5% in 2002 and by 6.5% in 2010
13. 13. Usual Suspects Skill-Biased Technological Change • In 2002 an additional year of education is estimated to add 10.0% to earnings • In 2010 this is only 8.4% • This coefficient overestimates the upper educational levels. • In 2002 an additional year of experience is estimated to add 5.4% to earnings (peaking at 38.4 years of experience) • In 2010 this is 5.1% (peaking at 37.8 years of experience) 012345 LogEarnings 5 10 15 20 25 Education in Years Individual Log Earnings Upper Limit 95% Confidence Interval Estimated Log Earnings given Education holding experience constant (at mean) Lower Limit 95% Confidence Interval 2002 UK Log Earnings and Education 02468 LogCPIAdjustedEarnings(in2002£) 5 10 15 20 25 Education in Years Individual Log Earnings Upper Limit 95% Confidence Interval Estimated Log Earnings given Education holding experience constant (at mean) Lower Limit 95% Confidence Interval 2010 CPI Adjusted Log Earnings and Education
14. 14. Usual Suspects Skill-Biased Technological Change2002 2010 _cons .1682376 .0386905 4.35 0.000 .0923955 .2440798 exp2 -.0007475 .0000228 -32.83 0.000 -.0007921 -.0007028 exp .0537047 .0014191 37.85 0.000 .0509231 .0564864 educ .099628 .0020078 49.62 0.000 .0956922 .1035638 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2873.27978 9125 .314879976 Root MSE = .49146 Adj R-squared = 0.2329 Residual 2203.22702 9122 .241528943 R-squared = 0.2332 Model 670.052766 3 223.350922 Prob > F = 0.0000 F( 3, 9122) = 924.74 Source SS df MS Number of obs = 9126 _cons .3863562 .026951 14.34 0.000 .33353 .4391825 exp2 -.0006751 .0000174 -38.83 0.000 -.0007092 -.000641 exp .0510363 .0010966 46.54 0.000 .0488868 .0531858 educ .0844415 .0013007 64.92 0.000 .0818921 .086991 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 6308.19149 19890 .317153921 Root MSE = .50294 Adj R-squared = 0.2025 Residual 5030.30389 19887 .25294433 R-squared = 0.2026 Model 1277.8876 3 425.962533 Prob > F = 0.0000 F( 3, 19887) = 1684.02 Source SS df MS Number of obs = 19891 • The explanative power of education and experience is decreasing (R2 = 0.23 in 2002 and R2 = 0.20 in 2010) Log(earnings) = β0 + β1*Education + β2*Experience + β3 *Experience^2
15. 15. Usual Suspects • The usual suspects (asc, sbtc) do not account for the increase in inequality • The usual suspects account for less of the facts (asc: 10.7% in 2002, 6.9% in 2010; sbtc 23.3% in 2002, 20.3% in 2010) • Old categories of sociologists and economists seem to have become increasingly inadequate irrelevant in explaining trends of inequality.
16. 16. Other Theories • Our inequality is caused by a spiral of political – economic connections, Stiglitz (2012) • Our inequality is worsened by lack of global accountability by politicians and financial elite have access to global diplomats (e.g. WTO), Pogge (2002) • i.e. income leads to (exclusive) connections, leads to more income
17. 17. Other Theories • Inspired by ‘For Richer (Not For Poorer): The Inequality Crisis of Marriage’ (The Atlantic, 14/03/12) • E.g. in Sweden Björklund (2012) estimates are that the income correlation coefficients 0.26 for the general population, and these rise to an extreme 0.96 when only the top 0.1% is considered.
18. 18. Other Theories .4 .45 .5 .55 .6 .65 ProbabilityMarried 0 1 2 3 4 Log CPI Adjusted Earnings (in 2002 £) Upper 95% Confidence Interval Lower 95% Confidence Interval Estimated Probability of being Married given Earnings holding age constant (at mean) 2010 Probability Married and Log Earnings for Mothers* .65 .7 .75 .8 0 1 2 3 4 Log Earnings Upper 95% Confidence Interval Lower 95% Confidence Interval Estimated Probability of being Married given Earnings holding age constant (at mean) 2002 UK Probability Married and Log Earnings for Mothers* _cons -1.779603 .2008241 -8.86 0.000 -2.173211 -1.385995 age .0586509 .0048839 12.01 0.000 .0490786 .0682232 logearning .1335943 .0668153 2.00 0.046 .0026386 .26455 married Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = -893.43794 Pseudo R2 = 0.0910 Prob > chi2 = 0.0000 LR chi2(2) = 178.81 Probit regression Number of obs = 1685 _cons -1.907614 .1414378 -13.49 0.000 -2.184827 -1.630401 age .0404978 .0031672 12.79 0.000 .0342903 .0467053 logearning .3999236 .0460117 8.69 0.000 .3097424 .4901048 married Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = -1905.968 Pseudo R2 = 0.0759 Prob > chi2 = 0.0000 LR chi2(2) = 313.22 Probit regression Number of obs = 3294
19. 19. Summary • Income inequality is increasing from the very top • The cause of income inequality is far from obvious – Labour models of discrimination, human capital, or sbtc less relevant • Hopefully interesting topic for our discussion
20. 20. Discussion • What is your experience with/opinion of income inequality? • What in your experience/opinion causes income inequality? • How can micro/macro economics help us understand the changes in income inequality? – Skill Biased Technological Change (contrary to evidence) – Economies of scale (why now?) – Information (herding behaviour) (why now?) – Winner-takes-all (who? just luck?)